Nowadays, high technologies are taking more and more important role in the process of taking the most important decisions. Information processing by super-powered cluster computers yield results comparable to usefulness of mining. The students, who write their research paper on data mining, have to by all means take into account the value of the knowledge discovery processes in the modern world. In their studies, students of universities and colleges are required to examine strictly the etymology of the term “data mining”, study the history of the emergence of this phenomenon and its evolution. It is very important to identify carefully all the methods that make up the process of data mining, to navigate freely in the wilds of this complicated issue. Your own ideas on how to further develop the technology, which in the future will be more and more widespread, will certainly be appreciated.
To write a good research paper on data mining as well as data warehousing, the investigators should focus on comparing the critical components that compile the totality of the knowledge discovering methods. You should be able to analyze all the nuances that can be recognized only by painstaking inspection. But it is not enough to know perfectly the subject. The results of your research should be present in such a manner so that not one small part of your immense research left undisclosed. To find out how to approach proper writing of a first-class research paper, you can familiarize yourself with sample research papers. These free examples will teach you how to write good research proposals on data mining techniques. You can find a lot of them on the Web, but be cautious to choose.
Most reports on bleeding complications caused by anticoagulants and/or antiplatelets are of warfarin and/or aspirin, with little information available for other antiplatelets, e.g., cilostazol, clopidogrel, ethyl icosapentate, limaprost alfadex, sarpogrelate and ticlopidine This study was conducted to assess the bleeding complications induced by the administration of antiplatelets and to attempt to determine the rank-order of the association, using more than a million case reports on adverse events (AERs) submitted to the US Food and Drug Administration (FDA). Authorized pharmacovigilance methods were used for quantitative signal detection [-], where a signal means a drug-associated adverse event or an association between a drug and an adverse event. Here, 7 antiplatelets were compared with warfarin in terms of susceptibility to bleeding complications.
Input data for this study were taken from the public release of the FDA's Adverse Event Reporting System (AERS) database, which covers the period from the first quarter of 2004 through the end of 2009. The total number of reports used was 2,231,029. This database relies on reports of spontaneous adverse events by health professionals, consumers, and manufacturers. The data structure of AERS is in compliance with international safety reporting guidance, ICH E2B, consisting of 7 data sets; patient demographic and administrative information (DEMO), drug/biologic information (DRUG), adverse events (REAC), patient outcomes (OUTC), report sources (RPSR), drug therapy start and end dates (THER), and indications for use/diagnosis (INDI). The adverse events in REAC are coded using preferred terms (PTs) in the Medical Dictionary for Regulatory Activities (MedDRA) terminology. Here, version 13.0 of MedDRA was used.
The total number of co-occurrences with warfarin was 156,357 in the AERS database. The adverse events were listed when at least 1 of 4 indices met the criteria, and 736 adverse events were listed as warfarin-associated adverse events with 64,289 co-occurrences in total. Among the 736 events, 147 were bleeding complications. The worst 20 were ranked according to the number of co-occurrences (N), with the official PT terms of MedDRA ver. 13.0.
Data mining or intellectual data analysis is the term which means the complex of methods which are used for the identification of the earlier unknowns, non-trivial, practically useful and available for interpretation knowledge, which are necessary for the process of decision making in different spheres of human activity. The term was introduced in 1989 and has become widely used since then. On the basis of the method of data mining there are various methods of classification, modelling and predicting, based on the use of the decision tree learning, artificial neural network, genetic algorithm, evolutionary programming and content-addressable memory. Moreover, there are statistic methods which are used to collect information for the profound analysis of the found data.
When one is asked to prepare a data mining term paper, he will need to work hard to catch the idea of the topic and understand its effectiveness for the humanity. Data mining is extremely useful in sciences, especially physics, when due to the spontaneous calculations new theories can appear. So, if one wants to succeed in term paper writing, he will have to explain the meaning of data mining, write about its origin, historical background, the factors, which provoked it, the methodology and its purpose. A student should speak about the principle and structure of the process of data mining and demonstrate the direct examples of the information which has been caught due to the methods of data mining.
Data Mining is a set of interdisciplinary procedures for discovering beforehand undisclosed, significant, practically helpful, and accessible data patterns indispensable for decision making in different areas of human activity. A term invented by Gregory Pyatetsky-Shapiro in 1989.
In order to cope with such a complicated topic a student will definitely need to take advantage of a free example term paper on data mining found in the Internet and prepared by en experienced writer. Professional writers do serious job and provide students with high-quality free sample term papers on data mining and give them the chance to improve their knowledge about the topic and the whole process of writing, including the analysis of the problem and composition and format of the text.
The incidence of bleeding with use of warfarin was variable among the clinical reports, and the variation might be explained by many factors, including definition of bleeding, patient mixes with indications and risks of bleeding, targeted INR, treatment protocol, treatment setting and length of follow-up . The ARES database has an advantage in the use of well-organized authorized terms of MedDRA, although the incidence cannot be calculated in this analysis. Additionally, it should be noted that there is no credible counterfactual means, e.g., a randomized control group, to extract drug-associated adverse events as signals, and therefore disease-oriented adverse events can be listed as signals. The results can be biased by unmeasured confounding factors. Although the comparison of aspirin with clopidogrel possibly offsets them, a statistically well-organized methodology should be established to minimize their effects. In conclusion, the data strongly suggest the necessity of well-organized clinical studies with respect to antiplatelet-associated bleeding complications.
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